step_rownormalize_tss: Feature normalization step using total sum scaling

In scimo: Extra Recipes Steps for Dealing with Omics Data

Feature normalization step using total sum scaling

Description

Normalize a set of variables by converting them to proportion, making them sum to 1. Also known as simplex projection.

Usage

step_rownormalize_tss(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  res = NULL,
  skip = FALSE,
  id = rand_id("rownormalize_tss")
)

## S3 method for class 'step_rownormalize_tss'
tidy(x, ...)

Arguments

recipe	A recipe object. The step will be added to the sequence of operations for this recipe.
...	One or more selector functions to choose variables for this step. See selections() for more details.
role	Not used by this step since no new variables are created.
trained	A logical to indicate if the quantities for preprocessing have been estimated.
res	This parameter is only produced after the recipe has been trained.
skip	A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id	A character string that is unique to this step to identify it.
x	A step_rownormalize_tss object.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Author(s)

Antoine Bichat

Examples

rec <-
  recipe(Species ~ ., data = iris) %>%
  step_rownormalize_tss(all_numeric_predictors()) %>%
  prep()
rec
tidy(rec, 1)
bake(rec, new_data = NULL)

scimo documentation built on June 24, 2024, 5:17 p.m.